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These are known as adversarial attacks, and the specific examples that are misclassified are known as adversarial examples. There is a reasonably large body of work on finding adversarial examples, and on making CNNs more robust (i.e. less prone to these attacks). An example is the DeepFool algorithm, which can be used to find perturbations of data which ...


4

The 2015 article Cyclical Learning Rates for Training Neural Networks by Leslie N. Smith gives some good suggestions for finding an ideal range for the learning rate. The paper's primary focus is the benefit of using a learning rate schedule that varies learning rate cyclically between some lower and upper bound, instead of trying to choose a single fixed ...


4

The visualisation can be found in The need for small learning rates on large problems. This paper by D. Randall Wilson and Tony R. Martinez from 2001 investigates the role of learning rates in gradient descent algorithms. In general, different algorithms assign different meaning to the same word 'learning rate'. For example, the learning rate in a gradient ...


3

Those examples are called Adversarial Examples. I think it is important to understand why a CNN can be "tricked" like that: We often expect human-like behavior when a model has a human-like performance. That is similar for CNNs. We expect they decide like we do, i.e. we look for the shape of objects. However, as various experiments on common CNN ...


3

If you look into the top conferences on machine learning and neural networks, such as NeurIPS, ICLR, and ICML, you will find many papers related to neural networks and deep learning, given that these are still very hot/promising topics. However, occasionally, you will find accepted papers that do not involve neural networks. Here's a small list of them that ...


2

Yes, there is research on this topic. The field that studies it is known as affective computing (AC). Emotion recognition seems to be a specific problem in affective computing, i.e. the recognition of emotions, while AC is also concerned with giving machines the ability to convey emotions (in fact, this paper differentiates the two). There's also sentiment ...


1

I suggest you look into link prediction. I have had good luck with the StellarGraph library. They have several algorithms implemented, including GCN. Link prediction is a binary classification problem. Given two nodes, $v_i$ and $v_j$, does there exist a link between them? Using a library like StellarGraph will also produce node embeddings while performing ...


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